Networks and information systems are deployed in corporations, government, and healthcare. These network and information systems communicate confidential and proprietary information. The amount of confidential and proprietary information being collected by institutions and organizations is growing at an exponential rate, which is a challenge to manage. As a result, the confidential and proprietary information has become the primary target of malicious actors. Thus, a wide range of security systems have been deployed to safeguard the information and integrity of the network.
The unauthorized or anomalous access to network assets in an organization is commonly approached by using sparse security baselines, behavior outliers, and access controls. Typically, unauthorized access is prevented through access controls. Access controls are enabled by a default behavior of rules to grant access to the community of users within the same network.
In more sophisticated cases, anomaly detection relies on a “supervised” approach that depends on the reliable and accurate classification of a training dataset. More specifically, the determination of anomalous and normal behavior depends on the quality of the classification model based on feature extraction from historical collections of events and metrics. However, the frequency of user interactions renders the supervised approach unsuitable for dynamic environments because the communications among users are continuously evolving and changing.
Other existing systems calculate a statistical baseline value, determine a type of access to the network assets, and detect deviations from the baseline value. However, the likelihood of detecting unauthorized network access is low because the unauthorized network access may be missed because of the long tail of the event logs. Thus, protection mechanisms based on sparse static baselining of user behavior is insufficient to detect internal threats.
The problem with static baselines, access control methods, and supervised anomaly detection includes the lack of support and context for highly dynamic user communities. Also, the static nature of access control methods and supervised anomaly detection present a problem in large networks due to the amount of workforce required to sustain an adequate security posture. Additionally, security monitoring systems are not designed to model and monitor network anomalies of user communities using exchange messages.
Another problem with traditional anomaly detection is the large number of false positives. In this situation, cybersecurity analysts must identify, protect, detect, and respond to a massive number of alerts, events, and metrics on the network including misleading, false positives. Moreover, traditional anomaly detection is inadequate in a growing and dynamic network of users.
It would, therefore, be beneficial to provide flexible security management systems and methods that detect unauthorized access to network assets based on an independent and dynamic set of user parameters or attributes.